file_tag = sprintf("%s_BL_%s", cell_type_name, graph_weight)
assayed_genes = scan(sprintf("output/gene_list_%s.txt", file_tag),
what = character(), sep="\n")
gene_sets = scan(sprintf("output/name_s_%s.txt", file_tag),
what = character(), sep="\n")
gene_sets = sapply(gene_sets, strsplit, USE.NAMES=FALSE, split=",")
n_genes = sapply(gene_sets, length)
names(n_genes) = NULL
summary(n_genes)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 14.00 15.00 14.35 15.00 17.00
## [1] 40
## [1] 5 11 12 13 13 13 14 14 14 14 14 14 14 14 14 14 14 14 15 15 15 15 15 15 15
## [26] 15 15 15 15 15 15 15 15 15 16 16 16 17 17 17
bioMart.All the gene symbols that can be found in bioMart are
consistent with what we have. So no need to run it.
ensembl = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
gene_BM = getBM(attributes = c("hgnc_symbol", "external_gene_name"),
filters = "external_gene_name",
values = assayed_genes,
mart = ensembl)
length(assayed_genes)
dim(gene_BM)
gene_BM[1:2,]
table(assayed_genes %in% gene_BM$external_gene_name)
t1 = table(gene_BM$external_gene_name)
dup = names(t1)[t1 > 1]
gene_BM[gene_BM$external_gene_name %in% dup,]
table(gene_BM$hgnc_symbol == gene_BM$external_gene_name)
w2kp = which(gene_BM$hgnc_symbol != gene_BM$external_gene_name)
gene_BM[w2kp,]alias2Symbol function from
limma.a2s = rep(NA, length(assayed_genes))
for(i in 1:length(assayed_genes)){
gi = assayed_genes[i]
ai = alias2Symbol(gi)
if(length(ai) > 1){
print(gi)
print(ai)
}
a2s[i] = ai[1]
}## [1] "QARS"
## [1] "EPRS1" "QARS1"
## [1] "SEPT2"
## [1] "SEPTIN6" "SEPTIN2"
##
## FALSE TRUE
## 1607 42
##
## FALSE TRUE <NA>
## 42 1565 42
gene_info = data.table(sym_in_data = assayed_genes, sym_limma = a2s)
gene_info[sym_in_data != sym_limma,]## sym_in_data sym_limma
## 1: C10orf91 LINC02870
## 2: C12orf10 MYG1
## 3: C12orf45 NOPCHAP1
## 4: C6orf48 SNHG32
## 5: C6orf99 LINC02901
## 6: CXorf40A EOLA1
## 7: CXorf57 RADX
## 8: FAM102A EEIG1
## 9: FAM173A ANTKMT
## 10: FAM213B PRXL2B
## 11: H2AFX H2AX
## 12: HIST1H2AG H2AC11
## 13: HIST1H2BK H2BC12
## 14: HIST1H2BN H2BC15
## 15: HIST1H3A H3C1
## 16: HIST1H3H H3C10
## 17: HIST1H4C H4C3
## 18: HIST2H2BF H2BC18
## 19: KIAA0391 PRORP
## 20: QARS EPRS1
## 21: SEPT6 SEPTIN6
## 22: ARNTL BMAL1
## 23: C12orf65 MTRFR
## 24: C16orf72 HAPSTR1
## 25: CCDC84 CENATAC
## 26: DOPEY2 DOP1B
## 27: FAM126B HYCC2
## 28: FAM160B1 FHIP2A
## 29: H1FX H1-10
## 30: H2AFJ H2AJ
## 31: HEXDC HEXD
## 32: HIST1H1C H1-2
## 33: HIST1H1D H1-3
## 34: HIST1H1E H1-4
## 35: KIAA1109 BLTP1
## 36: KIAA1551 RESF1
## 37: MKL1 MRTFA
## 38: NARFL CIAO3
## 39: SEPT2 SEPTIN6
## 40: TARSL2 TARS3
## 41: TMEM8A PGAP6
## 42: WDR60 DYNC2I1
## sym_in_data sym_limma
gene_info[, gene_symbol := sym_in_data]
gene_info[which(sym_in_data != sym_limma), gene_symbol := sym_limma]
dim(gene_info)## [1] 1649 3
## sym_in_data sym_limma gene_symbol
## 1: ABLIM1 ABLIM1 ABLIM1
## 2: AC004687.1 <NA> AC004687.1
## 3: AC004854.2 <NA> AC004854.2
## 4: AC007384.1 <NA> AC007384.1
## 5: AC007952.4 <NA> AC007952.4
## t1
## 1 2
## 1647 1
## sym_in_data sym_limma gene_symbol
## 1: SEPT6 SEPTIN6 SEPTIN6
## 2: SEPT2 SEPTIN6 SEPTIN6
Gene set annotations (by gene symbols) were downloaded from MSigDB website.
gmtfile = list()
gmtfile[["reactome"]] = "../Annotation/c2.cp.reactome.v2023.2.Hs.symbols.gmt"
gmtfile[["go_bp"]] = "../Annotation/c5.go.bp.v2023.2.Hs.symbols.gmt"
gmtfile[["immune"]] = "../Annotation/c7.all.v2023.2.Hs.symbols.gmt"
pathways = list()
for(k1 in names(gmtfile)){
pathways[[k1]] = gmtPathways(gmtfile[[k1]])
}
names(pathways)## [1] "reactome" "go_bp" "immune"
## reactome go_bp immune
## 1692 7647 5219
Filter gene sets for size between 10 and 500.
## $reactome
## 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
## 5.0 7.0 9.0 12.0 17.0 23.0 31.0 44.0 71.8 120.9 1463.0
##
## $go_bp
## 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
## 5.0 6.0 8.0 10.0 14.0 19.0 29.0 46.0 80.8 183.0 1966.0
##
## $immune
## 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
## 5 162 193 197 199 199 200 200 200 200 1992
## [1] 1649 3
max_n2kp = 10
goseq_res = NULL
for(k in 1:length(gene_sets)){
if(length(gene_sets[[k]]) < 10) { next }
print(k)
set_k = paste0("set_", k)
print(gene_sets[[k]])
genes = gene_info$sym_in_data %in% gene_sets[[k]]
names(genes) = gene_info$gene_symbol
table(genes)
pwf = nullp(genes, "hg38", "geneSymbol")
for(k1 in names(pathways)){
p1 = pathways[[k1]]
res1 = goseq(pwf, "hg38", "geneSymbol",
gene2cat=goseq:::reversemapping(p1))
res1$FDR = p.adjust(res1$over_represented_pvalue, method="BH")
nD = sum(res1$FDR < 0.1)
if(nD > 0){
res1 = res1[order(res1$FDR),][1:min(nD, max_n2kp),]
res1$category = gsub("REACTOME_|GOBP_", "", res1$category)
res1$category = gsub("_", " ", res1$category)
res1$category = tolower(res1$category)
res1$category = substr(res1$category, start=1, stop=81)
goseq_res[[set_k]][[k1]] = res1
}
}
}## [1] 1
## [1] "AC087623.3" "AMD1" "CITED2" "COG5" "GLTP"
## [6] "IFRD1" "ITGAE" "MAPRE2" "RSRP1" "THAP9-AS1"
## [11] "WSB1" "ZFP36L1" "NEAT1" "RASGRP1"
## [1] 2
## [1] "CCL4L2" "ARHGEF3" "ARL4C" "CLEC16A" "COL6A2" "COL6A3" "DDIT4"
## [8] "ISG20" "ITM2A" "LAIR2" "LPIN1" "REXO1" "TARSL2" "TRAV4"
## [15] "XCL1"
## [1] 3
## [1] "ATP5F1A" "C10orf91" "COQ8A" "EPB41L4A-AS1" "SNHG12"
## [6] "BROX" "DMTF1" "MTERF2" "PCED1B" "PPP4R3B"
## [11] "PTGDS" "RNF19A" "SLF2" "TMEM127" "TRBV7-6"
## [16] "TSPAN32" "VPS13C"
## [1] 4
## [1] "AC245297.3" "AF213884.3" "CXorf40A" "KCNQ1OT1" "KMT2E-AS1"
## [6] "MATR3-1" "NPIPB4" "TRAV8-4" "TRBV28" "TRBV7-9"
## [11] "TRGV7" "GK5" "MIAT" "PARP15" "SPATA13"
## [1] 5
## [1] "AC044849.1" "MZF1-AS1" "TRBV6-1" "ABCC10" "AP005482.1"
## [6] "CPPED1" "DENND4B" "ELMOD3" "LINC02256" "MINDY2"
## [11] "NBEAL2" "SEC14L1" "THUMPD3-AS1" "TRBV2" "TRGV10"
## [16] "Z93930.2" "ZNF808"
## [1] 6
## [1] "C12orf45" "CD28" "GSTM1" "NR1D1" "PDE7A" "TSPYL4"
## [7] "ANKRD12" "ANKRD36C" "NRDC" "PCSK7" "PPM1K" "SLFN12L"
## [13] "TRIM38" "UNC13D" "VTI1A"
## [1] 7
## [1] "CAMK4" "KCTD7" "KLRK1" "MAP3K2" "MZT2B" "NUAK2" "WARS2" "CHD9"
## [9] "ERICH1" "IRF9" "MYO1F" "NLRC3" "RASA3" "STK10"
## [1] 8
## [1] "ALKBH7" "C6orf48" "EPS8" "RCAN3" "TXK" "GNPTAB" "KLF2"
## [8] "MAN2C1" "PDE4B" "RIC3" "RNF125" "SCRN3" "TOB1" "TRGV4"
## [15] "ZBP1"
## [1] 9
## [1] "SNHG9" "ADCY7" "CCDC84" "ERBIN" "FAM78A"
## [6] "HPS4" "KIAA1109" "NUTM2B-AS1" "RAB27B" "SIDT1"
## [11] "SLCO3A1" "SYTL3" "TRDV1" "TTC38" "ZNF652"
## [1] 10
## [1] "ABCA5" "AC092683.1" "DDX3Y" "EIF1AY" "HECTD4"
## [6] "KDM5D" "RPS4Y1" "SBNO2" "TRAV17" "TTTY15"
## [11] "UTY"
## [1] 11
## [1] "BEX4" "CCR7" "CD40LG" "CREBL2" "CRTAM" "FAM102A"
## [7] "FXYD7" "HIST1H3H" "SDR42E2" "WDR86" "EIF4E3" "PIK3CD"
## [13] "PYROXD1" "RUBCN" "SAMD9L" "TTC16"
## [1] 12
## [1] "LEF1" "SELL" "ZFAND1" "ACAP3" "ATAD2" "CARD11" "CHD1"
## [8] "FAM53B" "MSI2" "PIP4K2B" "PKD1" "RNF157" "VCAN"
## [1] 13
## [1] "CHRM3-AS2" "RETREG1" "BMT2" "FAM133B" "HRH2" "IGKV3-15"
## [7] "LINC02384" "PUM3" "S100A12" "TENT5C" "THAP5" "TRAV12-3"
## [13] "TRAV19" "TRBV11-2" "TRBV4-2"
## [1] 14
## [1] "AL118516.1" "APMAP" "CMC1" "EFCAB2" "FAM173A"
## [6] "KIR3DL2" "MATK" "PITPNC1" "PRR7" "IQGAP2"
## [11] "RLF" "TBC1D2B" "TRGC2" "TUT7" "XIST"
## [1] 15
## [1] "LINC00402" "SESN2" "YPEL3" "CX3CR1" "GCA" "LSS"
## [7] "MKL1" "NLRC5" "ODF3B" "PATL2" "PDZD4" "PEX26"
## [13] "SZT2" "ZMIZ2" "ZNF557"
## [1] 16
## [1] "GALNT11" "MYLIP" "ANKRD36" "ARHGAP10" "ASCL2" "CD46"
## [7] "GON4L" "MBD5" "PDE4D" "PHF14" "PLAC8" "RAP1GAP2"
## [13] "SENP7" "TTC17"
## [1] 17
## [1] "ASL" "FAM213B" "AC020659.1" "C2CD3" "CCDC112"
## [6] "DDX60L" "EPSTI1" "ETFDH" "IFI44L" "IRAK4"
## [11] "MX2" "RREB1" "TRDC" "XAF1"
## [1] 18
## [1] "LRRN3" "PAPSS1" "PDCD4-AS1" "TBCCD1" "TESPA1" "TMEM204"
## [7] "TRAV21" "DOCK10" "ERAP2" "FGFBP2" "GALNT3" "IFI27"
## [13] "MIGA1" "OAS2" "ZNF683"
## [1] 19
## [1] "CLDND1" "CPNE1" "GZMK" "HIKESHI" "KIF9" "PIK3IP1" "SLC38A2"
## [8] "UIMC1" "APOL6" "TUT4" "VPS13B" "ZBTB40" "ZDHHC5"
## [1] 20
## [1] "NXT2" "TCEA3" "ZNF575" "ABR" "ARAP1" "BCL9L" "C5orf24"
## [8] "FAM13B" "GPR141" "GRK2" "INPP4A" "INPP5D" "SSBP3" "TRAV27"
## [15] "ZNF292" "ZNF493"
## [1] 21
## [1] "AC245014.3" "AL138963.3" "DTHD1" "LST1" "NBPF14"
## [6] "RGL4" "TC2N" "TRAV13-1" "TRAV8-2" "TRBV9"
## [11] "C16orf72" "GABPB2" "HECA" "PCNX1" "ZNF236"
## [1] 22
## [1] "AC008555.5" "ARRDC2" "LRRC23" "NOCT" "NUP58"
## [6] "PGGHG" "TRAV14DV4" "TRGV8" "ZNF749" "AC116407.2"
## [11] "BICRAL" "GPR132" "MYBL1" "POLR2J3-1" "TRANK1"
## [1] 23
## [1] "AIF1" "CD27" "GIMAP1" "MSC" "PCMTD2" "PDE3B" "PLK2"
## [8] "SLC38A1" "TMEM107" "CASP10" "COX19" "DUS1L" "ITGAM" "YPEL1"
## [1] 24
## [1] "AC119396.1" "ANXA2R" "BNIP3L" "C12orf10" "CMTM7"
## [6] "DYRK4" "EPHX2" "HIBADH" "NT5C3B" "PPP1R15B"
## [11] "RGCC" "SNHG15" "SNHG8" "ZNF10"
## [1] 25
## [1] "BTG1" "HIST1H4C" "LTB" "RGS10" "TRBC1" "CD38"
## [7] "GBP1" "GBP5" "IFITM2" "LAG3" "MIDN" "NKG7"
## [13] "SLA2" "STK17B"
## [1] 26
## [1] "AC004687.1" "CD84" "CXXC5" "IER5" "NCR1"
## [6] "RAB33B" "RPS26" "TCP11L2" "TOX" "TRAV5"
## [11] "ZFAS1" "CYTOR" "MCTP2" "RUFY2"
## [1] 27
## [1] "IL6R" "NOSIP" "ARHGEF9" "CISH" "CLUH" "GPHN" "IL6ST"
## [8] "MYO9A" "NAA25" "OSM" "PARP9" "RALGAPB" "SYNRG" "ZNF318"
## [15] "ZNF407"
## [1] 28
## [1] "BTG2" "CCNB1IP1" "GCSAM" "LDLRAP1" "SERINC5" "TCF7"
## [7] "WDR54" "ABCA7" "DIAPH2" "EHMT1" "HIPK3" "KIAA2026"
## [13] "NEK9" "USP16"
## [1] 29
## [1] "COA1" "SLC2A3" "TMIGD2" "AP3B1" "AP3M2" "CEMIP2"
## [7] "EHBP1L1" "FGL2" "HEXDC" "INO80D" "LINC02446" "MAF"
## [13] "OXNAD1" "TMEM131L"
## [1] 30
## [1] "AC004854.2" "AC087239.1" "AL136454.1" "AL627171.1" "BX284668.6"
## [6] "LINC02273" "MCUB" "MFNG" "RGS1" "Z93241.1"
## [11] "CRYBG1" "GCN1" "GDPD5" "MARF1" "MICAL2"
## [16] "PSMA3-AS1" "ZNF83"
## [1] 31
## [1] "AC012645.3" "AC016405.3" "AC020911.2" "AC027644.3" "AC083798.2"
## [6] "AC091271.1" "AK5" "AL135791.1" "ARF4-AS1" "LINC01465"
## [11] "PRAG1" "TRAV12-2" "TRAV8-3" "TRBV3-1" "TRBV6-2"
## [16] "ZNF862"
## [1] 32
## [1] "CARHSP1" "CRLF3" "CYB561A3" "NT5DC1" "ARAP2" "ARHGAP30"
## [7] "DGKD" "HIPK1" "HSH2D" "NCKAP1L" "NECAP1" "PRR14L"
## [13] "R3HCC1L" "SETX" "TMEM8A"
## [1] 33
## [1] "AC015982.1" "AC025164.1" "AC083880.1" "AL121944.1" "AL451085.1"
## [6] "ARMH1" "ATP2B1-AS1" "CSKMT" "CXorf57" "HIPK1-AS1"
## [11] "ILF3-DT" "INPP4B" "OSER1-DT" "TRABD2A"
## [1] 34
## [1] "SESN1" "TRBV20-1" "TRBV7-2" "ADGRE5" "AKNA"
## [6] "ARHGAP45" "ENOSF1" "IQCG" "NFATC3" "OGA"
## [11] "PIK3R5" "SLC16A1-AS1" "SUSD6" "TRAV9-2" "XCL2"
## [1] 35
## [1] "AOAH" "FAM118A" "HLA-DMB" "KLRC3" "KLRF1"
## [6] "MTRNR2L8" "TRAV38-2DV8" "TRBV6-5" "ADGRG1" "FAM126B"
## [11] "FCRL6" "LILRB1" "TMEM181"
## [1] 36
## [1] "AC007952.4" "AC103591.3" "AL357060.1" "BBS9" "C6orf99"
## [6] "HELQ" "IGKV3-20" "IGLV1-44" "INTS6L" "JAML"
## [11] "LINC00649" "TNFRSF25" "TRAV1-2" "TRAV3" "TRGV5"
## [1] 37
## [1] "AC007384.1" "AC025171.3" "AL139246.5" "ARL4A" "GADD45B"
## [6] "ID1" "NR4A2" "NR4A3" "AC016831.7" "ELMO1"
## [11] "FAM160B1" "GPRIN3" "NARFL" "PRR5L" "ZBTB20"
## [1] 39
## [1] "ARHGAP9" "EOMES" "FCRL3" "TRG-AS1" "TRGV9" "ATAD2B" "CCL4"
## [8] "CREBZF" "CST7" "CTSW" "GPR174" "ITGAL" "MYO1G" "NFE2L1"
## [1] 40
## [1] "ADAM17" "ADHFE1" "CD226" "CHD6" "ETNK1" "LONP2" "MPPE1"
## [8] "SEMA4D" "ST8SIA4" "SUSD1" "TGFBR3" "WDTC1"
for(n1 in names(goseq_res)){
k = as.numeric(gsub("set_", "", n1))
print(n1)
print(gene_sets[[k]])
print(goseq_res[[n1]])
}## [1] "set_2"
## [1] "CCL4L2" "ARHGEF3" "ARL4C" "CLEC16A" "COL6A2" "COL6A3" "DDIT4"
## [8] "ISG20" "ITM2A" "LAIR2" "LPIN1" "REXO1" "TARSL2" "TRAV4"
## [15] "XCL1"
## $reactome
## category
## 159 collagen biosynthesis and modifying enzymes
## 160 collagen chain trimerization
## 590 ncam1 interactions
## 72 assembly of collagen fibrils and other multimeric structures
## 162 collagen formation
## over_represented_pvalue under_represented_pvalue numDEInCat numInCat
## 159 8.097194e-05 1.0000000 2 2
## 160 8.097194e-05 1.0000000 2 2
## 590 2.416333e-04 0.9999994 2 3
## 72 2.416852e-04 0.9999994 2 3
## 162 2.416852e-04 0.9999994 2 3
## FDR
## 159 0.04708518
## 160 0.04708518
## 590 0.05621598
## 72 0.05621598
## 162 0.05621598
##
## [1] "set_8"
## [1] "ALKBH7" "C6orf48" "EPS8" "RCAN3" "TXK" "GNPTAB" "KLF2"
## [8] "MAN2C1" "PDE4B" "RIC3" "RNF125" "SCRN3" "TOB1" "TRGV4"
## [15] "ZBP1"
## $immune
## category over_represented_pvalue
## 1245 gse17974 0h vs 2h in vitro act cd4 tcell up 1.28182e-05
## under_represented_pvalue numDEInCat numInCat FDR
## 1245 0.9999996 5 41 0.06534718
##
## [1] "set_10"
## [1] "ABCA5" "AC092683.1" "DDX3Y" "EIF1AY" "HECTD4"
## [6] "KDM5D" "RPS4Y1" "SBNO2" "TRAV17" "TTTY15"
## [11] "UTY"
## $immune
## category
## 4352 gse5099 classical m1 vs alternative m2 macrophage dn
## over_represented_pvalue under_represented_pvalue numDEInCat numInCat
## 4352 3.246038e-06 1 4 20
## FDR
## 4352 0.0165483
##
## [1] "set_17"
## [1] "ASL" "FAM213B" "AC020659.1" "C2CD3" "CCDC112"
## [6] "DDX60L" "EPSTI1" "ETFDH" "IFI44L" "IRAK4"
## [11] "MX2" "RREB1" "TRDC" "XAF1"
## $immune
## category
## 12 erwin cohen blood vaccine tc 83 age 23 48yo vaccinated vs control 7dy up
## 383 gse13485 day1 vs day7 yf17d vaccine pbmc dn
## 472 gse14000 unstim vs 4h lps dc translated rna dn
## 381 gse13485 day1 vs day3 yf17d vaccine pbmc dn
## 10 erwin cohen blood tc 83 age 23 48yo vaccinated vs control 2dy up
## 1344 gse18791 unstim vs newcatsle virus dc 18h dn
## 375 gse13485 ctrl vs day3 yf17d vaccine pbmc dn
## 471 gse14000 unstim vs 4h lps dc dn
## 391 gse13485 pre vs post yf17d vaccination pbmc dn
## 387 gse13485 day3 vs day7 yf17d vaccine pbmc dn
## over_represented_pvalue under_represented_pvalue numDEInCat numInCat
## 12 4.887953e-06 0.9999999 5 36
## 383 1.175114e-05 0.9999996 5 41
## 472 1.239645e-05 0.9999996 5 44
## 381 1.357466e-05 0.9999997 4 21
## 10 1.641272e-05 0.9999995 5 45
## 1344 1.707362e-05 0.9999994 5 45
## 375 1.805806e-05 0.9999994 5 46
## 471 2.699099e-05 0.9999990 5 51
## 391 2.753164e-05 0.9999990 5 49
## 387 3.496352e-05 0.9999986 5 53
## FDR
## 12 0.01315143
## 383 0.01315143
## 472 0.01315143
## 381 0.01315143
## 10 0.01315143
## 1344 0.01315143
## 375 0.01315143
## 471 0.01388303
## 391 0.01388303
## 387 0.01388303
##
## [1] "set_27"
## [1] "IL6R" "NOSIP" "ARHGEF9" "CISH" "CLUH" "GPHN" "IL6ST"
## [8] "MYO9A" "NAA25" "OSM" "PARP9" "RALGAPB" "SYNRG" "ZNF318"
## [15] "ZNF407"
## $go_bp
## category
## 2937 positive regulation of tyrosine phosphorylation of stat protein
## 4595 tyrosine phosphorylation of stat protein
## over_represented_pvalue under_represented_pvalue numDEInCat numInCat
## 2937 1.570789e-06 1.0000000 4 9
## 4595 4.838054e-06 0.9999999 4 11
## FDR
## 2937 0.007368573
## 4595 0.011347655
##
## [1] "set_35"
## [1] "AOAH" "FAM118A" "HLA-DMB" "KLRC3" "KLRF1"
## [6] "MTRNR2L8" "TRAV38-2DV8" "TRBV6-5" "ADGRG1" "FAM126B"
## [11] "FCRL6" "LILRB1" "TMEM181"
## $immune
## category
## 4250 gse45739 unstim vs acd3 acd28 stim nras ko cd4 tcell dn
## over_represented_pvalue under_represented_pvalue numDEInCat numInCat
## 4250 9.944221e-06 0.9999997 5 54
## FDR
## 4250 0.05069564
##
## [1] "set_36"
## [1] "AC007952.4" "AC103591.3" "AL357060.1" "BBS9" "C6orf99"
## [6] "HELQ" "IGKV3-20" "IGLV1-44" "INTS6L" "JAML"
## [11] "LINC00649" "TNFRSF25" "TRAV1-2" "TRAV3" "TRGV5"
## $reactome
## category
## 180 creation of c4 and c2 activators
## 444 initial triggering of complement
## 900 scavenging of heme from plasma
## 132 cell surface interactions at the vascular wall
## 121 cd22 mediated bcr regulation
## 435 immunoregulatory interactions between a lymphoid and a non lymphoid cell
## 310 fcgr activation
## 163 complement cascade
## 872 role of phospholipids in phagocytosis
## 96 binding and uptake of ligands by scavenger receptors
## over_represented_pvalue under_represented_pvalue numDEInCat numInCat
## 180 0.0001102635 0.9999998 2 4
## 444 0.0001102635 0.9999998 2 4
## 900 0.0001102635 0.9999998 2 4
## 132 0.0001161525 0.9999988 3 25
## 121 0.0001809758 0.9999995 2 5
## 435 0.0003021830 0.9999954 3 34
## 310 0.0003794161 0.9999982 2 7
## 163 0.0003797855 0.9999982 2 7
## 872 0.0003823279 0.9999982 2 7
## 96 0.0003835763 0.9999981 2 7
## FDR
## 180 0.03377133
## 444 0.03377133
## 900 0.03377133
## 132 0.03377133
## 121 0.04209496
## 435 0.04460992
## 310 0.04460992
## 163 0.04460992
## 872 0.04460992
## 96 0.04460992
## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 8958253 478.5 16391124 875.4 NA 16391124 875.4
## Vcells 19167796 146.3 59973464 457.6 65536 77218972 589.2
## R version 4.2.3 (2023-03-15)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.4.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] TxDb.Hsapiens.UCSC.hg38.knownGene_3.16.0
## [2] GenomicFeatures_1.50.4
## [3] GenomicRanges_1.50.2
## [4] GenomeInfoDb_1.34.9
## [5] org.Hs.eg.db_3.16.0
## [6] AnnotationDbi_1.60.2
## [7] IRanges_2.32.0
## [8] S4Vectors_0.36.2
## [9] Biobase_2.58.0
## [10] BiocGenerics_0.44.0
## [11] goseq_1.50.0
## [12] geneLenDataBase_1.34.0
## [13] BiasedUrn_2.0.10
## [14] fgsea_1.24.0
## [15] biomaRt_2.54.1
## [16] limma_3.54.2
## [17] tidyr_1.3.0
## [18] ggpubr_0.6.0
## [19] ggplot2_3.4.2
## [20] data.table_1.14.8
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-162 matrixStats_1.0.0
## [3] bitops_1.0-7 bit64_4.0.5
## [5] filelock_1.0.2 progress_1.2.2
## [7] httr_1.4.6 tools_4.2.3
## [9] backports_1.4.1 bslib_0.4.2
## [11] utf8_1.2.3 R6_2.5.1
## [13] mgcv_1.8-42 DBI_1.1.3
## [15] colorspace_2.1-0 withr_2.5.0
## [17] tidyselect_1.2.0 prettyunits_1.1.1
## [19] bit_4.0.5 curl_5.0.1
## [21] compiler_4.2.3 cli_3.6.1
## [23] xml2_1.3.4 DelayedArray_0.24.0
## [25] rtracklayer_1.58.0 sass_0.4.5
## [27] scales_1.2.1 rappdirs_0.3.3
## [29] Rsamtools_2.14.0 stringr_1.5.0
## [31] digest_0.6.31 rmarkdown_2.21
## [33] XVector_0.38.0 pkgconfig_2.0.3
## [35] htmltools_0.5.5 MatrixGenerics_1.10.0
## [37] dbplyr_2.3.2 fastmap_1.1.1
## [39] rlang_1.1.0 rstudioapi_0.14
## [41] RSQLite_2.3.1 BiocIO_1.8.0
## [43] jquerylib_0.1.4 generics_0.1.3
## [45] jsonlite_1.8.4 BiocParallel_1.32.6
## [47] dplyr_1.1.2 car_3.1-2
## [49] RCurl_1.98-1.12 magrittr_2.0.3
## [51] GO.db_3.16.0 GenomeInfoDbData_1.2.9
## [53] Matrix_1.6-4 Rcpp_1.0.10
## [55] munsell_0.5.0 fansi_1.0.4
## [57] abind_1.4-5 lifecycle_1.0.3
## [59] stringi_1.7.12 yaml_2.3.7
## [61] carData_3.0-5 SummarizedExperiment_1.28.0
## [63] zlibbioc_1.44.0 BiocFileCache_2.6.1
## [65] grid_4.2.3 blob_1.2.4
## [67] parallel_4.2.3 crayon_1.5.2
## [69] lattice_0.20-45 splines_4.2.3
## [71] Biostrings_2.66.0 cowplot_1.1.1
## [73] hms_1.1.3 KEGGREST_1.38.0
## [75] knitr_1.44 pillar_1.9.0
## [77] rjson_0.2.21 ggsignif_0.6.4
## [79] codetools_0.2-19 fastmatch_1.1-3
## [81] XML_3.99-0.14 glue_1.6.2
## [83] evaluate_0.20 png_0.1-8
## [85] vctrs_0.6.2 gtable_0.3.3
## [87] purrr_1.0.1 cachem_1.0.7
## [89] xfun_0.39 broom_1.0.4
## [91] restfulr_0.0.15 rstatix_0.7.2
## [93] tibble_3.2.1 GenomicAlignments_1.34.1
## [95] memoise_2.0.1